# Copyright (c) 2023-2024 DeepSeek.
#
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# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
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# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
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#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

from threading import Thread
from typing import List

import torch
import transformers
from joblib.externals.cloudpickle import instance
from transformers import (
    AutoModelForCausalLM,
    StoppingCriteria,
    StoppingCriteriaList,
    TextIteratorStreamer,
)

from deepseek_vl2.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM
from deepseek_vl2.models.conversation import Conversation


def load_model(model_path, dtype=torch.bfloat16):
    vl_chat_processor = DeepseekVLV2Processor.from_pretrained(model_path)
    tokenizer = vl_chat_processor.tokenizer

    vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(
        model_path, trust_remote_code=True, torch_dtype=dtype
    )
    vl_gpt = vl_gpt.cuda().eval()
    return tokenizer, vl_gpt, vl_chat_processor


def convert_conversation_to_prompts(conversation: Conversation):
    conv_prompts = []

    last_image = None

    messages = conversation.messages
    for i in range(0, len(messages), 2):

        if isinstance(messages[i][1], tuple):
            text, images = messages[i][1]
            last_image = images[-1]
        else:
            text, images = messages[i][1], []

        prompt = {
            "role": messages[i][0],
            "content": text,
            "images": images
        }
        response = {"role": messages[i + 1][0], "content": messages[i + 1][1]}
        conv_prompts.extend([prompt, response])

    return conv_prompts, last_image


class StoppingCriteriaSub(StoppingCriteria):
    def __init__(self, stops=[], encounters=1):
        super().__init__()
        self.stops = [stop.to("cuda") for stop in stops]

    def __call__(
        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
    ):
        for stop in self.stops:
            if input_ids.shape[-1] < len(stop):
                continue
            if torch.all((stop == input_ids[0][-len(stop) :])).item():
                return True

        return False


@torch.inference_mode()
def deepseek_generate(
    conversations: list,
    vl_gpt: torch.nn.Module,
    vl_chat_processor: DeepseekVLV2Processor,
    tokenizer: transformers.PreTrainedTokenizer,
    stop_words: list,
    max_length: int = 256,
    temperature: float = 1.0,
    top_p: float = 1.0,
    repetition_penalty: float = 1.1,
    chunk_size: int = -1
):
    pil_images = []
    for message in conversations:
        if "images" not in message:
            continue
        pil_images.extend(message["images"])

    prepare_inputs = vl_chat_processor.__call__(
        conversations=conversations,
        images=pil_images,
        inference_mode=True,
        force_batchify=True,
        system_prompt=""
    ).to(vl_gpt.device)

    return generate(
        vl_gpt,
        tokenizer,
        prepare_inputs,
        max_gen_len=max_length,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        top_p=top_p,
        stop_words=stop_words,
        chunk_size=chunk_size
    )


@torch.inference_mode()
def generate(
    vl_gpt,
    tokenizer,
    prepare_inputs,
    max_gen_len: int = 256,
    temperature: float = 0,
    repetition_penalty=1.1,
    top_p: float = 0.95,
    stop_words: List[str] = [],
    chunk_size: int = -1
):
    """Stream the text output from the multimodality model with prompt and image inputs."""
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)

    stop_words_ids = [
        torch.tensor(tokenizer.encode(stop_word)) for stop_word in stop_words
    ]
    stopping_criteria = StoppingCriteriaList(
        [StoppingCriteriaSub(stops=stop_words_ids)]
    )

    if chunk_size != -1:
        inputs_embeds, past_key_values = vl_gpt.incremental_prefilling(
            input_ids=prepare_inputs.input_ids,
            images=prepare_inputs.images,
            images_seq_mask=prepare_inputs.images_seq_mask,
            images_spatial_crop=prepare_inputs.images_spatial_crop,
            attention_mask=prepare_inputs.attention_mask,
            chunk_size=chunk_size
        )
    else:
        inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
        past_key_values = None

    generation_config = dict(
        inputs_embeds=inputs_embeds,
        input_ids=prepare_inputs.input_ids,
        images=prepare_inputs.images,
        images_seq_mask=prepare_inputs.images_seq_mask,
        images_spatial_crop=prepare_inputs.images_spatial_crop,
        attention_mask=prepare_inputs.attention_mask,
        past_key_values=past_key_values,
        pad_token_id=tokenizer.eos_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=max_gen_len,
        do_sample=True,
        use_cache=True,
        streamer=streamer,
        stopping_criteria=stopping_criteria,
    )

    if temperature > 0:
        generation_config.update(
            {
                "do_sample": True,
                "top_p": top_p,
                "temperature": temperature,
                "repetition_penalty": repetition_penalty,
            }
        )
    else:
        generation_config["do_sample"] = False

    thread = Thread(target=vl_gpt.generate, kwargs=generation_config)
    thread.start()

    yield from streamer
